High-performance computing (HPC) is often characterized by the computing systems used by scientists and engineers for modeling, simulating, and analyzing complex physical or algorithmic phenomena. Currently, HPC machines are typically designed using Numerous HPC clusters of one or more processors referred to as nodes. For most large scientific and engineering applications, performance is chiefly determined by parallel scalability and not the speed of individual nodes; therefore, scalability is often a limiting factor in building or purchasing such high-performance clusters. Scalability is generally considered to be based on i) hardware, ii) memory, input/output (I/O), and communication bandwidth; iii) software; iv) architecture; and v) applications. The processing, memory, and I/O bandwidth in most conventional HPC environments are normally not well balanced and, therefore, do not scale well. Many HPC environments do not have the I/O bandwidth to satisfy high-end data processing requirements or are built with blades that have too many unneeded components installed, which tend to dramatically reduce the system's reliability. Accordingly, many HPC environments may not provide robust cluster management software for efficient operation in production-oriented environments.